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Development of an Improved Genetic Algorithm for Resolving Inverse Kinematics of Virtual Human’s Upper Limb Kinematics Chain

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Applied Methods and Techniques for Mechatronic Systems

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 452))

Abstract

Inverse kinematics is the key technique in virtual human motion control and it is difficult to obtain the solutions by using geometric, algebraic, or iterative algorithms. In this chapter, an Improved Genetic Algorithm (IGA) is proposed to resolve the inverse kinematics problem in upper limb kinematics chain (ULKC). First, the joint-units of ULKC and its mathematical models are constructed by using D–H method; then population diversity and population initialization are accomplished by simulating human population, and the adaptive operators for mutation are designed. The simulation results show that compared with the Standard Genetic Algorithm (SGA), the IGA can provide higher precise solutions in searching process and avoid “premature” stop or inefficient searching in later stage with high probability.

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Acknowledgments

This work is partially supported by the National Nature Science Foundation of China under Grant 61102170 and 61273188, and the National Advanced Research Foundation of China under Grant 9140A27040112JB47081.

Author information

Authors and Affiliations

  1. Xi’an Research Institute of High Technology, Xi’an, 710025, People’s Republic of China

    Gangfeng Deng, Xianxiang Huang, Qinhe Gao & Ying Zhan

  2. College of Chemical Engineering, China University of Petroleum, Qingdao, 266580, People’s Republic of China

    Quanmin Zhu

  3. Department of Engineering Design and Mathematics, University of the West of England, Coldharbour Lane, Bristol BS16 1QY, UK

    Quanmin Zhu

Authors
  1. Gangfeng Deng
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  2. Xianxiang Huang
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  3. Qinhe Gao
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  4. Ying Zhan
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  5. Quanmin Zhu
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Corresponding author

Correspondence to Gangfeng Deng .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, People's Republic of China

    Lei Liu

  2. University of the West of England, Bristol, United Kingdom

    Quanmin Zhu

  3. Wuhan University of Science and Technology, Wuhan, People's Republic of China

    Lei Cheng

  4. Huazhong University of Science and Technology, Wuhan, People's Republic of China

    Yongji Wang

  5. China University of Petroleum, Qingdao, People's Republic of China

    Dongya Zhao

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Deng, G., Huang, X., Gao, Q., Zhan, Y., Zhu, Q. (2014). Development of an Improved Genetic Algorithm for Resolving Inverse Kinematics of Virtual Human’s Upper Limb Kinematics Chain. In: Liu, L., Zhu, Q., Cheng, L., Wang, Y., Zhao, D. (eds) Applied Methods and Techniques for Mechatronic Systems. Lecture Notes in Control and Information Sciences, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36385-6_10

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  • DOI: https://doi.org/10.1007/978-3-642-36385-6_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36384-9

  • Online ISBN: 978-3-642-36385-6

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